update samples from Release-54 as a part of SDK release

Author: vizhur

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -4,15 +4,14 @@
 
 from azureml.core.run import Run
 from azureml.core.experiment import Experiment
-from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations, automl_check_model_if_explainable
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from azureml.automl.core.shared.constants import MODEL_PATH
-from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
-
+from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer
+import joblib
 
 OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)
@@ -74,7 +73,8 @@
 scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])
 
 # Pickle scoring explainer locally
-save(scoring_explainer, exist_ok=True)
+with open('scoring_explainer.pkl', 'wb') as stream:
+    joblib.dump(scoring_explainer, stream)
 
 # Upload the scoring explainer to the automl run
 automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')